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Abstract #5144

DeepRelaxo: A Generalizable Self-supervised Method for Brain R2* Mapping

Samiha Prima1, Zhuang Xiong1, Alan H. Wilman2, and Hongfu Sun1
1The University of Queensland, Brisbane, Australia, 2University of Alberta, Edmonton, AB, Canada

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: R2* is a marker to monitor the progression of neurodegenerative diseases. Existing algorithms face challenges with varying acquisition parameters, Rician noise levels, and undersampling. Extended scanning times hinder R2* implementation in clinical settings.

Goal(s): To introduce a framework that achieves parameter generalizability and noise robustness for R2* mapping without the requirement of in-vivo dataset .

Approach: We propose DeepRelaxo, a cascaded framework of transformer followed by a 3D-UNet.

Results: DeepRelaxo is trained on a synthetic dataset with varying number of echoes, echo times, spacing, and rician noise levels. It significantly outperforms NLLS in shorter echoes, particularly in 2-echo R2* reconstructions with high noise.

Impact: Self-supervised training removes the need for real-world scans and allows DeepRelaxo adaptable across diverse clinical sites. Improved shorter echo reconstructions facilitates significantly scan time reduction.

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